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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Use Spark SQL for data processing |
| Topic 2: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries |
| Topic 3: Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components |
| Topic 4: Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Implement incremental data processing - Design data pipelines for batch and streaming |
| Topic 5: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Use PySpark for data processing - Work with Spark APIs in Python |
| Topic 6: Delta Lake Fundamentals | 20-25% | - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables |
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NEW QUESTION # 274
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
Answer: B
Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. Reference: Running SQL Queries Programmatically, Formatted string literals, spark.sql
NEW QUESTION # 275
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark UI and can see the ERRORS as follows:
"java.lang.OutofMemoryError: Java heap space"
Which two corrective actions should the data engineer perform to resolve this issue? (Choose two.)
Answer: B,C
Explanation:
Reducing input with narrower filters lowers memory pressure, and increasing executor resources (upsize worker nodes) plus enabling adaptive/auto shuffle partitioning provides more memory and better partitioning to avoid Java heap OOM during Spark SQL execution.
NEW QUESTION # 276
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
Answer: C
Explanation:
Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a
NEW QUESTION # 277
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution. Which compute option should the data engineer use?
Answer: A
Explanation:
A Serverless SQL Warehouse automatically scales to handle fluctuating workloads, requires no infrastructure management, and charges only for the compute used during query execution, making it cost-efficient for large datasets.
NEW QUESTION # 278
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?
Answer: D
Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the DatabricksLakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.
NEW QUESTION # 279
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